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The environmental justice literature demonstrates consistently that low‐income and minority communities are disproportionately exposed to environmental hazards. In this case study, we examined cumulative multipollutant, multidomain, and multimatrix environmental exposures in Milwaukee County, Wisconsin for the year 2015. We identified spatial hot spots in Milwaukee County both individually (using local Moran's I) and through clusters (using K‐means clustering) across a profile of environmental pollutants that span regulatory domains and matrices of exposure, as well as socioeconomic indicators. The cluster with the highest exposures within the urban area was largely characterized by low socioeconomic status and an overrepresentation of the Non‐Hispanic Black population relative to the county as a whole. In this cluster, average pollutant concentrations were equivalent to the 78th percentile in county‐level blood lead levels, 67th percentile in county‐level NO2, 79th percentile in county‐level CO, and 78th percentile in county‐level air toxics. Simultaneously, this cluster had an average equivalent to the 62nd percentile in county‐level unemployment, 70th percentile in county‐level population rate lacking a high school diploma, 73rd percentile in county‐level poverty rate, and 28th percentile in county‐level median household income. The spatial patterns of pollutant exposure and SES indicators suggested that these disparities were not random but were instead structured by socioeconomic and racial factors. Our case study, which combines environmental pollutant exposures, sociodemographic data, and clustering analysis, provides a roadmap to identify and target overburdened communities for interventions that reduce environmental exposures and consequently improve public health. Plain Language Summary: Our study focused on Milwaukee County, Wisconsin, where we examined how people in this region were exposed to different types of pollutants. We found that areas with the highest levels of pollution (e.g., lead, nitrogen dioxide) had a higher proportion of Black residents and residents that experienced social and economic challenges (e.g., unemployment, poverty, and low education). Our work adds to the growing evidence that patterns of pollution and economic challenges are not random, but rather, racially and socially structured. By understanding these patterns, we can develop policies that reduce pollution in these areas and improve the health for residents in these overburdened communities. Key Points: We examine cumulative exposures to multiple pollutants and their association with socioeconomic and racial disparities in Milwaukee CountyWe highlight census block groups that are most vulnerable to pollution and low socioeconomic status (SES), which can be prioritized for regulatory interventionsPeople of color in Milwaukee County are not just exposed to high pollution, they are often exposed within the context of low SES [ABSTRACT FROM AUTHOR]

Children's health studies support an association between maternal environmental exposures and children's birth outcomes. A common goal is to identify critical windows of susceptibility—periods during gestation with increased association between maternal exposures and a future outcome. The timing of the critical windows and magnitude of the associations are likely heterogeneous across different levels of individual, family, and neighborhood characteristics. Using an administrative Colorado birth cohort we estimate the individualized relationship between weekly exposures to fine particulate matter (PM2.5) during gestation and birth weight. To achieve this goal, we propose a statistical learning method combining distributed lag models and Bayesian additive regression trees to estimate critical windows at the individual level and identify characteristics that induce heterogeneity from a high-dimensional set of potential modifying factors. We find evidence of heterogeneity in the PM2.5—birth weight relationship, with some mother—child dyads showing a three times larger decrease in birth weight for an IQR increase in exposure (5.9–8.5 μ g / m 3 PM2.5) compared to the population average. Specifically, we find increased vulnerability for non-Hispanic mothers who are either younger, have higher body mass index or lower educational attainment. Our case study is the first precision health study of critical windows. for this article are available online. [ABSTRACT FROM AUTHOR]

Distributed lag models are useful in environmental epidemiology as they allow the user to investigate critical windows of exposure, defined as the time periods during which exposure to a pollutant adversely affects health outcomes. Recent studies have focused on estimating the health effects of a large number of environmental exposures, or an environmental mixture, on health outcomes. In such settings, it is important to understand which environmental exposures affect a particular outcome, while acknowledging the possibility that different exposures have different critical windows. Further, in studies of environmental mixtures, it is important to identify interactions among exposures and to account for the fact that this interaction may occur between two exposures having different critical windows. Exposure to one exposure early in time could cause an individual to be more or less susceptible to another exposure later in time. We propose a Bayesian model to estimate the temporal effects of a large number of exposures on an outcome. We use spike-and-slab priors and semiparametric distributed lag curves to identify important exposures and exposure interactions and discuss extensions with improved power to detect harmful exposures. We then apply these methods to estimate the effects of exposure to multiple air pollutants during pregnancy on birthweight from vital records in Colorado. [ABSTRACT FROM AUTHOR]

Introduction Chinese cities face some of the worst urban air pollution in the world, in part because of China's rapid economic expansion and industrial development (Chan and Yao 2008). This [...], Background: Studies found approximately linear short-term associations between particulate matter (PM) and mortality in Western communities. However, in China, where the urban PM levels are typically considerably higher than in Western communities, some studies suggest nonlinearity in this association. Health impact assessments (HIA) of PM in China have generally not incorporated nonlinearity in the concentration-response (C-R) association, which could result in large discrepancies in estimates of excess deaths if the true association is nonlinear. Objectives: We investigated nonlinearity in the C-R associations between with PM with aerodynamic diameter [less than or equal to]2.5 [micro]m ([PM.sub.2.5]) and mortality in Beijing, China, and the sensitivity of HIA to linearity assumptions. Methods: We modeled the C-R association between [PM.sub.2.5] and cause-specific mortality in Beijing, China (2009- 2012), using generalized linear models (GLM). [PM.sub.2.5] was included through either linear, piecewise-linear, or spline functions to investigate evidence of nonlinearity. To determine the sensitivity of HIA to linearity assumptions, we estimated [PM.sub.2.5]-attributable deaths using both linear- and nonlinear-based C-R associations between [PM.sub.2.5] and mortality. Results: We found some evidence that, for nonaccidental and circulatory mortality, the shape of the C-R association was relatively flat at lower concentrations of [PM.sub.2.5], but then had a positive slope at higher concentrations, indicating nonlinearity. Conversely, the shape for respiratory mortality was positive and linear at lower concentrations of [PM.sub.2.5], but then leveled off at the higher concentrations. Estimates of excess deaths attributable to short-term [PM.sub.2.5] exposure were, in some cases, very sensitive to the linearity assumption in the association, but in other cases robust to this assumption. Conclusions: Our results demonstrate some evidence of nonlinearity in [PM.sub.2.5]-mortality associations and that an assumption of linearity in this association can influence HIAs, highlighting the importance of understanding potential nonlinearity in the [PM.sub.2.5]-mortality association at the high concentrations of [PM.sub.2.5] in developing megacities like Beijing. https://doi.org/10.1289/EHP4464

Exposure to air pollution is associated with increased morbidity and mortality. Recent technological advancements permit the collection of time‐resolved personal exposure data. Such data are often incomplete with missing observations and exposures below the limit of detection, which limit their use in health effects studies. In this paper, we develop an infinite hidden Markov model for multiple asynchronous multivariate time series with missing data. Our model is designed to include covariates that can inform transitions among hidden states. We implement beam sampling, a combination of slice sampling and dynamic programming, to sample the hidden states, and a Bayesian multiple imputation algorithm to impute missing data. In simulation studies, our model excels in estimating hidden states and state‐specific means and imputing observations that are missing at random or below the limit of detection. We validate our imputation approach on data from the Fort Collins Commuter Study. We show that the estimated hidden states improve imputations for data that are missing at random compared to existing approaches. In a case study of the Fort Collins Commuter Study, we describe the inferential gains obtained from our model including improved imputation of missing data and the ability to identify shared patterns in activity and exposure among repeated sampling days for individuals and among distinct individuals. [ABSTRACT FROM AUTHOR]

Distributed lag models (DLMs) are often used to estimate lagged associations and identify critical exposure windows. In a simulation study of prenatal nitrogen dioxide (NO2) exposure and birth weight, we demonstrate that bias amplification and variance inflation can manifest under certain combinations of DLM estimation approaches and time-trend adjustment methods when using low-spatial-resolution exposures with extended lags. Our simulations showed that when using high-spatial-resolution exposure data, any time-trend adjustment method produced low bias and nominal coverage for the distributed lag estimator. When using either low- or no-spatial-resolution exposures, bias due to time trends was amplified for all adjustment methods. Variance inflation was higher in low- or no-spatial-resolution DLMs when using a long-term spline to adjust for seasonality and long-term trends due to concurvity between a distributed lag function and secular function of time. NO2–birth weight analyses in a Massachusetts-based cohort showed that associations were negative for exposures experienced in gestational weeks 15–30 when using high-spatial-resolution DLMs; however, associations were null and positive for DLMs with low- and no-spatial-resolution exposures, respectively, which is likely due to bias amplification. DLM analyses should jointly consider the spatial resolution of exposure data and the parameterizations of the time trend adjustment and lag constraints. [ABSTRACT FROM AUTHOR]